The present invention relates to a system for classifying seafloor roughness using artificial neural network (ANN) hybrid layout from unprocessed multi-beam backscatter data. More particularly, the present invention relates to a system for online seafloor roughness classification from unprocessed multi-beam angular backscatter data using unsupervised learning as a pre-processor and supervised learning as the concluding block for improved classification, resulting in a highly efficient hybrid neural network layout to classify an unclassified dataset.
Hitherto known neural classifier for seafloor classification [Z. Michalopoulou, D. Alexandrou, and C. de Moustier, xe2x80x9cApplication of Neural and Statistical Classifiers to the Problem of Seafloor Characterizationxe2x80x9d, IEEE Journal of Oceanic Engineering, Vol. 20, pp. 190-197 (1994)] describes a self-organizing map (SOM) network that is applied to multi-beam backscatter dataset. The drawback of this system is that it can use only processed data. Another drawback is its unsuitability for on-line application.
An alternate system [B. Chakraborty, R. Kaustubha, A. Hegde, A. Pereira, xe2x80x9cAcoustic Seafloor Sediment Classification Using Self Organizing Feature Mapsxe2x80x9d, IEEE Transactions Geoscience and Remote Sensing, Vol. 39, No. 12, pp. 2722-2725 (2001)] describes a SOM network wherein single-beam dataset is used for seafloor classification, and this system is more suited to online use. However, a limitation of this system is that it requires pre-processing of the time-series dataset prior to classification.
In U.S. patent application Ser. No. 09/814,104 the Applicants have described a system which is incorporated in seafloor classification. This system described in this application estimates the seafloor acoustic backscattering strength with recorded root-mean-square (r.m.s) echo-voltage and the signal duration for each beam. In this system, multi-beam angular backscatter data have been acquired from the various seafloor areas around the Indian Ocean using a multi-beam acoustic system (Hydrosweep) installed onboard the Ocean Research Vessel Sagar Kanya. A drawback of the aforesaid system is that it requires large time-overhead to correct the raw data for range-related gain, seafloor slope correction, and insonification-depth normalization.
Yet another system [B. Chakraborty, H. W. Schenke, V. Kodagali, and R. Hagen, xe2x80x9cSeabottom Characterization Using Multi-beam Echo-sounder: An Application of the Composite Roughness Theoryxe2x80x9d, IEEE Transactions Geoscience and Remote Sensing, Vol. 38, pp. 2419-2422 (2000)] describes a system for seafloor classification, wherein it has been observed that the seafloor roughness parameters (power-law parameters) are the ideal parameters for classification. The drawback of this system is that seafloor classification can be implemented only after carrying out physical modeling of composite roughness parameters.
The main object of the present invention is to provide a novel system for seafloor classification using artificial neural network (ANN) hybrid layout with the use of unprocessed multi-beam backscatter data.
Another object of the present invention is to provide a system for on-line (i.e., real-time) seafloor classification using backscatter data after training the self-organized mapping (SOM) network and learning vector quantization (LVQ) network.
Yet another object of the present invention is to provides a system that incorporates a hybrid network using unsupervised SOM as the first block for coarse classification of the seafloor backscatter data and supervised LVQ for highly improved performance in the said classification.
Still another object of the present invention is to provide a system which incorporates a combination of two variations of the LVQ layout to work together to achieve the best classification results.
The present invention relates to a system for classifying seafloor roughness using artificial neural network (ANN) hybrid layout from unprocessed multi-beam backscatter data. More particularly, the present invention relates to a system for online seafloor roughness classification from unprocessed multi-beam angular backscatter data using unsupervised learning as a pre-processor and supervised learning as the concluding block for improved classification, resulting in a highly efficient hybrid neural network layout to classify an unclassified dataset.
Accordingly, the present invention provides a system for classifying seafloor roughness using artificial neural network (ANN) hybrid layout from unprocessed multi-beam backscatter data, said system comprising a means for generating unprocessed multi-beam backscatter r.m.s. data attached to the input of a self-organizing map (SOM) preprocessor (20), said SOM preprocessor being attached through one or more Learning Vector Quantization (LVQ) variants (21 and 23) to a memory/display module (22).
In an embodiment of the present invention, the means for generating unprocessed multi-beam backscatter r.m.s. data comprises a multi-beam acoustic device mounted beneath a ship""s hull and attached to an r.m.s. estimator module through a beam former module.
In another embodiment of the present invention, the multi-beam acoustic device comprises a linear array of transducers connected to a roll-pitch-heave sensor through cable connection boxes and an array of transmit-receive systems.
In yet another embodiment of the present invention, the multi-beam acoustic device comprises of two identical arrays of acoustic transducers mounted at right angles to each other.
In still another embodiment of the present invention, each array of the acoustic transducer is a combination of several sub-arrays and each sub array consists of multitude of elements.
In a further embodiment of the present invention, each element form a set of channels.
In one more embodiment of the present invention, the arrays can be used either for transmission or for reception of signals.
In one another embodiment of the present invention, the multi-beam acoustic device is connected to the beam former module through a preamplifier and a time varying gain adjustment circuit.
In an embodiment of the present invention, beam forming is accomplished using appropriate delays.
In another embodiment of the present invention, the beam former module is connected to the r.m.s. estimator module through a digital to analog converter, a filter, and a analog to digital converter.
In still another embodiment of the present invention, a display means is optionally connected to the analog to digital converter.
In yet another embodiment of the present invention, the display means is connected to the analog to digital converter through a bottom-tracking gate.
In a further embodiment of the present invention, the output pattern of the r.m.s. estimator module is the envelope of the r.m.s. signal amplitude Vs. beam number in cross-track direction.
In one more embodiment of the present invention, self-organizing map (SOM) preprocessor classifies the seafloor data into various roughness types and clusters them.
In one another embodiment of the present invention, the roughness parameters are distinguished based on the ship""s cross-track angular multi-beam signal backscatter shape parameter.
In an embodiment of the present invention, each cluster formed represents an unique pattern of the input data.
In another embodiment of the present invention, the number of clusters thus formed is equal to the number of differing patterns of received seafloor data set.
In yet another embodiment of the present invention, the clusters are formed with the inherent unsupervised learning feature of the SOM preprocessor.
In still another embodiment of the present invention, the clusters are formed without any prior knowledge of the number of the different types of input patterns.
In a further embodiment of the present invention, the Learning Vector Quantization (LVQ) variant overcomes the imperfection in classification arising from the process of unsupervised classification done by SOM preprocessor.
In one more embodiment of the present invention, the improvements seafloor classification is achieved by supervised learning.
In one another embodiment of the present invention, the supervised learning is imparted to the LVQ by a human interpreter based on the ground truth data set.
In an embodiment of the present invention, said system incorporates a LVQ designed to avoid misclassification at the central portion of the weight-distribution of each cluster or a LVQ designed to distinguish the overlapping tails of the weight distribution of adjacent clusters or a combination of both.
In another embodiment of the present invention, the LVQ designed to avoid misclassification at the central portion is based on xe2x80x9creward-punishmentxe2x80x9d criterion.
In yet another embodiment of the present invention, the LVQ moves the weight-vector from the input if it is wrongly represented and the weight-vector is made to match the input more closely if its correctly represented.
In still another embodiment of the present invention, the LVQ designed to distinguish the overlapping of tails employs the technique of redistribution of the weights of the overlapping portion of the adjacent clusters to the respective parent clusters.
In one more embodiment of the present invention, human interpreter can make use of the results displayed on the display device to make further judgements on the quality of classification.
The present invention more preferably provides a novel system for seafloor classification using artificial neural network (ANN) hybrid layout with the use of unprocessed multi-beam backscatter data, which comprises of an artificial neural network system that consists of a self-organizing map (SOM) preprocessor [20], learning vector quantization variants LVQ1 [21] and LVQ2 [23], and the memory/display module [22], wherein the input to the said SOM network [20] is derived from the output of an r.m.s. estimator module [19], which received its input from an A/D converter [16], which in turn acquired its input from a beam-former [13] attached to two identical perpendicularly oriented arrays [7] and [8] of a multi-beam acoustic device of FIG. 3, the said signal after having been rendered into analog format and filtered with the use of appropriate electronic hardware circuitry [14] and [15] respectively; the said SOM network [20] receiving the unprocessed multi-beam backscatter r.m.s data derived from the different pre-formed beams, and classifying the seafloor data into various roughness types that are distinguished based on the ship""s cross-track angular multi-beam signal backscatter shape parameter, wherein a unique cluster is formed to represent a specific pattern of the input data; the number of clusters thus formed being equal to the number of differing patterns of the received seafloor data set; the said clusters having been formed without any prior knowledge of the number of different types of input patterns; the said clusters of data set being subsequently input to the LVQ1 [21]; the imperfection in classification arising from the process of unsupervised learning without any background knowledge being partially overcome by the LVQ1 network [21] as in FIG. 5; the said improvement in seafloor classification achievable by supervised learning based on the feedback provided to the said LVQ1 network [21] by the human interpreter based on the ground truth data set using an appropriate weight-updating criterion, with a correctly-representing weight-vector having been made to match the input more strongly, while a wrongly-representing weight-vector having been moved away from the input, so as to avoid misclassification at the central portion of the weight-distribution of each cluster; the outputs of the said LVQ1 [21] being subsequently input to the memory and display module [22], wherein the results thereby displayed having the utility for human interpretation to be made to enhance the quality of classification; the system of the present invention having the capability to incorporate an alternate hybrid layout wherein the LVQ1 [21] module could be replaced by another supervisable module LVQ2 [23] as in FIG. 6 that performs the alternate function of distinguishing the overlapping tails of the weight distribution of adjacent clusters to implement seafloor classification with minimal error; said system having additional capability to incorporate an improved hybrid layout, the said layout having the advantage of implementing seafloor classification based on the differing abilities of LVQ1 [21] and LVQ2 [23] as in FIG. 7, thereby providing the best possible classification, taking into account the central as well as tail portions of the weight distributions in the process of classification.
In an embodiment of the present invention, the neural network layout is a model-independent system that would provides the capability to the use of unprocessed backscatter data from the seafloor for the purpose of classification.
In another embodiment of the present invention, online seafloor classification is possible subsequent to the training phase of the network, thereby providing a cost-effective system having the capability to circumvent the need for pre-processing of the raw data.
In yet another embodiment of the present invention, a one-dimensional (i.e., bar-plot) presentation of a multitude of self-organized clusters of unprocessed (i.e., raw) input dataset and subsequent classification are provided to the human interpreter for further judgment of the quality of classification, and additional capability of visualization of the received input vectors in real time.
In still another embodiment of the present invention, said capabilities are extendable to the processed backscatter data as well.
In a further embodiment of the present invention, said system is capable of real-time redirecting cross-track multi-beam angular backscatter data received from the echo-sounder installed onboard the ship/AUV, to a remote databank.
In one more embodiment of the present invention, said redirection is carried out by representation of an extensive dataset by a few clustering units as formed in the said system layout on a ping-by-ping basis.
Novelty and Inventive Step
The novelty and inventive step of the present invention provides for an ingenious system for seafloor classification using artificial neural network (ANN) hybrid layout with the use of unprocessed multi-beam backscatter data, thereby circumventing the need for the conventional laborious and time-consuming preprocessing task that would have been required otherwise. The system of the present invention allows on-line (i.e., real-time) seafloor classification using backscatter data after training the self-organized mapping (SOM) network and learning vector quantization (LVQ) network. Further, the system of the present invention has the unique capability for the combined application of unsupervised SOM followed by supervised LVQ to achieve a highly improved performance in the said classification, which is hitherto non-existent. The system of the present invention has the additional capability for the use of a combination of the two variations of the LVQ layout to work together to achieve the best results in seafloor classification.
The novel system for seafloor classification using artificial neural network (ANN) hybrid layout provides:
1. The capability of using unprocessed multi-beam backscatter data, thereby circumventing the need for the conventional laborious and time-consuming preprocessing task that would have been required otherwise.
2. The ability for on-line (i.e., real-time) seafloor classification after training the self-organized mapping (SOM) network and learning vector quantization (LVQ) network using a large time-series dataset in the absence of background information.
3. The unique capacity for the combined use of unsupervised SOM followed by supervised LVQ to achieve a highly improved performance in the said seafloor classification, which is hitherto non-existent.
4. The means for the use of a combination of the two variants of the LVQ layout, namely LVQ1 (that minimizes misclassification) and LVQ2 (that adjusts overlapping weights of adjacent clusters), to operate together to achieve the best results in seafloor classification.